Cell-to-cell differences in transcriptional or posttranslational regulation can give rise to heterogeneous phenotypes within a population (Slack et al. (2008) Proc Natl Acad Sci U.S.A. 105(49):19306-19311; Raj et al. (2010) Nature 463(7283):913-918; Singh et al. (2010) Mol Syst Biol 6:369; Wernet et al. (2006) Nature 440(7081):174-180; Laslo et al. (2006) Cell 126(4):755-766; Gupta et al. (2011) Cell 146(4):633-644; Tyson et al. (2012) Nat Methods 9(9):923-928). These changes in gene expression drive many biological processes. There are several techniques for monitoring regulatory states in single cells after a network of marker and effector genes has been identified (Loo et al. (2009) Nat Methods 6(10):759-765; Dalerba et al. (2011) Nat Biotechnol 29(12):1120-1127; Taniguchi et al. (2009) Nat Methods 6(7):503-506; Bendall et al. (2011) Science 332(6030):687-696; Raj et al. (2008) Nat Methods 5(10):877-879; Lubeck et al. (2012) Nat Methods 9(7):743-748). However, the options are much more limited when seeking to discover novel states without a predefined network.
At the transcript level, global methods have been developed to profile single cells by oligonucleotide microarrays (Kurimoto et al. (2006) Nucleic Acids Res 34(5):e42; Tietjen et al. (2003) Neuron 38(2):161-175) or RNA-seq (Hashimshony et al. (2012) Cell Rep 2(3):666-673; Ramskold et al. (2012) Nat Biotechnol 30(8):777-782; Tang et al. (2009) Nat Methods 6(5):377-382; Shalek et al. (2013) Nature 498(7453):236-240). But generally, such approaches overlook the considerable technical variation in RNA extraction and reverse-transcription when applied to the limited starting material of single cells (Wang et al. (2013) Nat Protoc 8(2):282-301; Reiter et al. (2011) Nucleic Acids Res 39(18):e124). Single-cell profiles also retain the biological noisiness associated with each cell's isolation and handling (Hansen et al. (2011) Nat Biotechnol 29(7):572-573). These confounding sources of variation cannot be separated from reproducible heterogeneities in regulation unless many (>50) cells are individually profiled (Dalerba et al. (2011) Nat Biotechnol 29(12):1120-1127). Therefore, challenges remain for single-cell methods to discover regulatory heterogeneities in a reliable, unbiased, and efficient way.
An attractive alternative to single-cell methods is to analyze sets of population-averaged data and define regulatory signatures for discrete subpopulations. Existing approaches for transcriptomic data are able to deconvolve mixed cellular states computationally, but they require hundreds of coexpressed markers or calibration with purified cell populations (Riedel et al. (2013) Phys Rev E 87(4):042715; Shen-Orr et al. (2010) Nat Methods 7(4):287-289; Gong et al. (2011) PLoS One 6(11):e27156). Usually, the size or identity of regulatory states is not defined beforehand and their discovery is what motivates the study (Dalerba et al. (2011) Biotechnol 29(12):1120-1127; Bendall et al. (2011) Science 332(6030):687-696; Loo et al. (2009) J Cell Biol 187(3):375-384). Certain states may also lack well-defined surface markers that would allow purification, it thus remains unclear whether computational inference with multiple cell averages can track quantitative characteristics of regulatory states not previously thought to exist.
As a hybrid between single-cell and mixture-based approaches, a technique that applies probability theory to transcriptome-wide measurements was developed (Janes et al. (2010) Nat Methods 7(4):311-317). The method begins with random collections of up to 10 cells isolated in situ where cell-to-cell regulatory heterogeneities could possibly reside. Each of these “stochastic samples” is then profiled for overall mRNA expression by using a heavily customized cDNA amplification procedure together with oligonucleotide microarrays (Wang et al. (2013) Nat Protoc 8(2):282-301). The process of random sampling is repeated 15-20 times to build a distribution of 10-cell averages. Transcripts with stark cell-to-cell variations can be distinguished statistically because of binomial fluctuations in single-cell expression that convolve their 10-cell averages. Last, candidate heterogeneities are clustered on a gene-by-gene basis according to the patterns of their sampling fluctuations to indicate putative regulatory states in single cells (Janes et al. (2010) Nat Methods 7(4):311-317).
Stochastic-profiling experiments are quantitative and highly reproducible as a result of the 10-fold increase in starting material compared to a single cell (Wang et al. (2013) Nat Protoc 8(2):282-301). However, a recognized drawback of the approach is that explicit information about single cells is “lost” in the 10-cell averages. While the transcriptome-wide identification of regulatory heterogeneities can be robustly achieved by randomly collecting small numbers of cells, the method blurs out the expression state of individual cells in each sample and thus, does not give an entirely accurate representation. There remains a need for acquiring quantitative, single-cell data from measurements that are not collected from single cells. Improved methods that can be applied to any type of biomolecule measured with a bioassay that is sensitive to tens of cells may lead to discovering regulatory heterogeneities in a number of biological contexts, such as development and cancer. A clear and quantified understanding of gene expression regulation would give scientists vital information for the development of therapies in every indication. Further, quantifying single-cell regulatory heterogeneities while avoiding the measurement noise of global single-cell techniques will he particularly relevant to solid tissues, where single-cell dissociation and molecular profiling is especially problematic.